Chebifier

Chebifier is a tool for automated classification of chemicals in the ChEBI ontology. This repository only hosts the front end of Chebifier. For the classification itself, see python-chebifier.

A web server running Chebifier is available here.

News

• 2026/08/18: Recalibrated ensemble (with ~500 new classes), added new deep learning models (v252) and model attributions. Now supports InChI input, user feedback, and extended ensemble settings.
• 2026/02/16: Added Lopster and new deep learning models.
• 2025/11/11: Fixed processing error for GNNs.
• 2025/11/05: Added new models (v244, including GAT, 3-STAR models and augmented GNNs), redesigned frontend.

Installation

Setup Frontend

Change to the respective directory and build the node.js files

cd react-app
npm run build

Run in development

You can now start the development server with

cd backend
flask run

The server should now run at localhost:5000, serving both the API and the built frontend. Start it from the backend directory - the configuration and data/disjoint_*.csv are looked up relative to the working directory. The first startup may take a bit longer: the ChEBI graph, the model checkpoints and the ChEBI lookup table are all loaded up front.

The backend has to run in an environment that has chebifier installed.

Setup Backend

Some dependencies require that pytorch is already installed:

pip install torch

After that, you can install the prediction system and web framework:

pip install -r backend/requirements.txt

config.template.json contains a template for a Chebifier configuration. Copy the contents of this file

cp backend/config.template.json backend/config.json

and change the path for each setting according to your setup. An example configuration is available on Hugging Face. Note that this does not touch the actual ensemble behaviour. For the ensemble, see python-chebifier.

To use the precision/recall sliders, you can download the csv table from Hugging Face and set the "PR_CURVE" parameter in the config file.

Windows: enable long paths

On the first startup the model checkpoints are downloaded from Hugging Face into the local cache. Some checkpoint paths exceed Windows' default 260-character MAX_PATH limit, which makes the download fail with a FileNotFoundError during build_base_learners. Enable long-path support once, in an Administrator PowerShell:

Set-ItemProperty -Path 'HKLM:\SYSTEM\CurrentControlSet\Control\FileSystem' -Name LongPathsEnabled -Value 1 -Type DWord

Then start a fresh terminal (the flag is read at process startup) before running the backend again; a reboot may be needed if a fresh shell still fails. Python 3.6+ is long-path-aware, so no code or cache changes are required.

Citation

If you found Chebifier useful, please cite: Martin Glauer, Fabian Neuhaus, Simon Flügel, Marie Wosny, Till Mossakowski, Adel Memariani, Johannes Schwerdt and Janna Hastings "Chebifier: Automating Semantic Classification in ChEBI to Accelerate Data-driven Discovery."Digital Discovery, 2024, 3, 896.